Blockchain Based Transparent Vehicle Insurance Management
Bibliographic record
Abstract
The automotive industry is re-blooming with recent enhances in technology. Electric vehicles and autonomous vehicles are already attracting attention to the industry and providing momentum for adoption of other emerging technologies. This has its impact on a diverse range of stakeholders from manufacturers to consumers.There is a new frontier that can lend its abilities to the experiences built around vehicles and it is the blockchain technology. Blockchain technology is an enabler. It can act as a transaction medium between interacting parties. It can also be used as a tamper-free ledger to store a history of transactions. With these two simple abilities, blockchains can enable several applications to make vehicle-related experiences better.In this paper, we propose a tamper-free ledger of events as an insurance record of motor vehicles. This insurance record system can include all aspects of insurance transactions. It not only would improve the experience around proving insurance, but also act as evidence in the event of a dispute. This ledger can have extended services around providing a clean driving record. Individual drivers, dealers, insurance companies, lawyers, law enforcement agencies and motor vehicle agencies are all stakeholders of this blockchain based solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".